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Finite-temperature calculations are relevant for rationalizing material properties yet they are computationally expensive because large system sizes or long simulation times are typically required. Circumventing the need for performing many…

Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of…

Chemical Physics · Physics 2020-12-09 Félix Musil , Michele Ceriotti

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for…

Chemical Physics · Physics 2021-03-16 Michael Gastegger , Jörg Behler , Philipp Marquetand

A Moment Tensor Potential (MTP) has been developed for the Cu-Ag binary alloy and its accuracy, transferability, and thermodynamic fidelity evaluated. The model was trained on a diverse dataset encompassing solid, liquid, and interfacial…

Materials Science · Physics 2025-08-26 Mashroor S. Nitol , Marco J. Echeverría Iriarte , Doyl E. Dickel , Saryu J. Fensin

An archetypical spin-glass metallic alloy, Cu0.83Mn0.17, is studied by means of an ab-initio based approach. First-principles calculations are employed to obtain effective chemical, strain-induced and magnetic exchange interactions, as well…

Materials Science · Physics 2009-06-16 O. E. Peil , A. V. Ruban , B. Johansson

We propose an efficient approach for simultaneous prediction of thermal and electronic transport properties in complex materials. Firstly, a highly efficient machine-learned neuroevolution potential is trained using reference data from…

Materials Science · Physics 2024-04-08 Zheyong Fan , Yang Xiao , Yanzhou Wang , Penghua Ying , Shunda Chen , Haikuan Dong

We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the…

Computational Physics · Physics 2020-08-06 Anh Tran , Julien Tranchida , Tim Wildey , Aidan P. Thompson

We assess the accuracy of six universal machine-learned interatomic potentials (MLIPs) for predicting the temperature and pressure response of materials by molecular dynamics simulations. Accuracy is evaluated across 13 diverse materials…

Materials Science · Physics 2025-12-01 Konstantin Stracke , Connor W. Edwards , Jack D. Evans

We present results of molecular dynamics simulations on lithium metasilicate over a broad range of temperatures for which the silicate network is frozen in but the lithium ions can still be equilibrated. The lithium dynamics is studied via…

Disordered Systems and Neural Networks · Physics 2017-08-23 Andreas Heuer , Magnus Kunow , Michael Vogel , Radha D. Banhatti

We have developed models of metallic alloy glass forming ability based on newly computationally accessible features obtained from molecular dynamics simulations. In this work we showed that it is possible to increase the predictive value of…

We present Molecular Dynamics simulations of the thermal glass transition in a dense model polymer liquid. We performed a comparative study of both constant volume and constant pressure cooling of the polymer melt. Great emphasis was laid…

Statistical Mechanics · Physics 2009-10-30 C. Bennemann , W. Paul , K. Binder , B. Duenweg

Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of $ab$ $initio$ methods and low accuracy of empirical potentials. We propose a computational technique towards…

Aluminum oxide (alumina, Al$_2$O$_3$) exists in various structures and has broad industrial applications. While the crystal structure of $\alpha$-Al$_2$O$_3$ is well-established, those of transitional aluminas remain highly debated. In this…

Materials Science · Physics 2025-02-11 Lei Zhang , Wenhao Luo , Renxi Liu , Mohan Chen , Zhongbo Yan , Kun Cao

By means of molecular dynamics simulations we examine the aging process of a strong glass former, a silica melt modeled by the BKS potential. The system is quenched from a temperature above to one below the critical temperature, and the…

Disordered Systems and Neural Networks · Physics 2007-05-23 H. Wahlen , H. Rieger

Owing to the excellent catalysis properties of Ag-Au binary nanoalloy, nanostructured Ag-Au, such as Ag-Au nanoparticles and nanopillars, have been under intense investigation. To achieve high accuracy in molecular simulations of the Ag-Au…

Materials Science · Physics 2021-08-16 Yinan Wang , Xiaoyang Wang , Linfeng Zhang , Ben Xu , Han Wang

This work is devoted to the \textit{ab initio} calculation of transport and optical properties of aluminum. The calculation is based on the quantum molecular dynamics simulation, density functional theory and the Kubo-Greenwood formula.…

Computational Physics · Physics 2013-10-16 D. V. Knyazev , P. R. Levashov

A scale bridging strategy based in molecular statics and molecular dynamics simulations in combination with transition state theory has been developed to determine the flow stress of Al-Cu alloy containing Guinier-Preston zones. The…

Materials Science · Physics 2019-09-04 G. Esteban-Manzanares , B. Bellón , E. Martínez , I. Papadimitriou , J. LLorca

The first--principles density functional molecular dynamics simulations have been carried out to investigate the geometric, the electronic, and the finite temperature properties of pure Li clusters (Li$_{10}$, Li$_{12}$) and Al--doped Li…

Mesoscale and Nanoscale Physics · Physics 2009-11-11 Mal--Soon Lee , S. Gowtham , Haiying He , Kah--Chun Lau , Lin Pan , D. G. Kanhere

The predictive accuracy of density functional theory (DFT) for alloy formation enthalpies is often limited by intrinsic energy resolution errors, particularly in ternary phase stability calculations. In this work, we present a machine…

Materials Science · Physics 2025-03-10 Sergei I. Simak , Erna K. Delczeg-Czirjak , Olle Eriksson

We address the degree to which machine learning can be used to accurately and transferably predict post-Hartree-Fock correlation energies. Refined strategies for feature design and selection are presented, and the molecular-orbital-based…

Chemical Physics · Physics 2019-04-17 Lixue Cheng , Matthew Welborn , Anders S. Christensen , Thomas F. Miller
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